The generation of surrogate data, i.e., the modification of data to destroy a certain feature, can be considered as the implementation of a null-hypothesis whenever an analytical approach is not feasible. Thus, surrogate data generation has been extensively used to assess the significance of spike correlations in parallel spike trains. In this context, one of the main challenges is to properly construct the desired null-hypothesis distribution and to avoid altering the single spike train statistics. A classical surrogate technique is uniform dithering (UD), which displaces spikes locally and uniformly distributed, to destroy temporal properties on a fine timescale while keeping them on a coarser one. Here, we compare UD against five similar surrogate techniques in the context of the detection of significant spatiotemporal spike patterns. We evaluate the surrogates for their performance, first on spike trains based on point process models with constant firing rate, and second on modeled nonstationary artificial data to assess the potential detection of false positive (FP) patterns in a more complex and realistic setting. We determine which statistical features of the spike trains are modified and to which extent. Moreover, we find that UD fails as an appropriate surrogate because it leads to a loss of spikes in the context of binning and clipping, and thus to a large number of FP patterns. The other surrogates achieve a better performance in detecting precisely timed higher-order correlations. Based on these insights, we analyze experimental data from the pre-/motor cortex of macaque monkeys during a reaching-and-grasping task.
Spatio-temporal spike patterns were suggested as indications of active cell assemblies. We developed the SPADE method to detect significant spatio-temporal patterns (STPs) with ms accuracy. STPs are defined as identically repeating spike patterns across neurons with temporal delays between the spikes. The significance of STPs is derived by comparison to the null-hypothesis of independence implemented by surrogate data. SPADE binarizes the spike trains and examines the data for STPs by counting repeated patterns using frequent itemset mining. The significance of STPs is evaluated by comparison to pattern counts derived from surrogate data, i.e., modifications of the original data with destroyed potential spike correlations but under conservation of the firing rate profiles. To avoid erroneous results, surrogate data are required to retain the statistical properties of the original data as much as possible. A classically chosen surrogate technique is Uniform Dithering (UD), which displaces each spike independently according to a uniform distribution. We find that binarized UD surrogates of our experimental data (motor cortex) contain fewer spikes than the binarized original data. As a consequence, false positives occur. Here, we identify the reason for the spike reduction, which is the lack of conservation of short ISIs. To overcome this problem, we study five alternative surrogate techniques and examine their statistical properties such as spike loss, ISI characteristics, effective movement of spikes, and arising false positives when applied to different ground truth data sets: first, on stationary point process models, and then on non-stationary point processes mimicking statistical properties of experimental data. We conclude that trial-shifting best preserves the features of the original data and has a low expected false-positive rate. Finally, the analysis of the experimental data provides consistent STPs across the alternative surrogates.
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